1. Field of the Invention
The present invention relates to an image diagnostic processing device and an image diagnostic processing program which performs a diagnostic process on anatomic abnormality such as nodule abnormality or wen abnormality in a three-dimensional image collected using a medical image diagnostic modality such as an X-ray computed tomographic imaging device, an X-ray diagnostic device, a magnetic resonance imaging device or an ultrasonic diagnostic device.
2. Description of the Related Art
Now, as lung cancer is the top cause of cancer death and is being increased in Japan, social demand for preventive medicine by smoking countermeasures and early detection has increased. In municipalities of Japan, an examination for lung cancer using a chest plain X-ray film and sputum cytodiagnosis has been performed, but, in a report “study group on effectiveness evaluation of an examination for cancer” of Ministry of Health and Welfare of Japan, published in 1998, the effect of the existing examination for the lung cancer is obtained, but is insignificant. In an X-ray computed tomography (hereinafter, referred to as CT), lung-field type lung cancer can be more easily detected than a chest plain X-ray film. However, before 1990, at which time a helical scanning type CT was developed, the CT cannot be used in the examination for cancer because imaging time is long. Shortly after the helical CT is developed, imaging method using relatively low X-ray tube current (hereinafter, referred to as a low dose helical CT) was developed so as to reduce exposed dose and pilot study on the examination for lung cancer using this method was made in Japan and the united states of America. As a result, it is verified that the low dose helical CT has a lung-cancer detection ratio significantly higher than that of the chest plain X-ray film.
Meanwhile, imaging time in the helical CT has been continuously reduced by multiple-row of the CT detector since 1998 and, in a recent multi-detector row helical CT, a whole lung image can be acquired within ten seconds with isotropic resolution less than 1 mm. The technical improvement of the CT enables us to detect smaller lung cancers. However, since the multi-detector row helical CT generates several hundreds of images per one scan, a burden required for interpretation of the images significantly increases.
Under such circumstances, in order to establish the low dose helical CT as a method of examining lung cancer, it is widely known that a computer assisted diagnosis (hereinafter, referred to as CAD) for preventing lung cancer from being overlooked is necessary. Since small lung-field-type lung cancer appears on a CT image as nodule-shaped abnormality, automatic detection of abnormality (hereinafter, referred to as automatic detection of a CT lung nodule) is of importance and various studies have been made since 1990s (for example, see “David S. Paik, et al., “Surface Normal Overlap: A Computer-Aided Detection Algorithm With Application to Colonic Polyps and Lung Nodules in Helical CT”, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol. 23, No. 6, June 2004, p 661-675”).